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Network meta-analysis combines aggregate data (AgD) from multiple randomisedcontrolled trials, assuming that any effect modifiers are balanced acrosspopulations. Individual patient data (IPD) meta-regression is the "goldstandard" method to relax this assumption, however IPD are frequently onlyavailable in a subset of studies. Multilevel network meta-regression (ML-NMR)extends IPD meta-regression to incorporate AgD studies whilst avoidingaggregation bias, but currently requires the aggregate-level likelihood to havea known closed form. Notably, this prevents application to time-to-eventoutcomes.  We extend ML-NMR to individual-level likelihoods of any form, by integratingthe individual-level likelihood function over the AgD covariate distributionsto obtain the respective marginal likelihood contributions. We illustrate withtwo examples of time-to-event outcomes, showing the performance of ML-NMR in asimulated comparison with little loss of precision from a full IPD analysis,and demonstrating flexible modelling of baseline hazards using cubic M-splineswith synthetic data on newly diagnosed multiple myeloma.  ML-NMR is a general method for synthesising individual and aggregate leveldata in networks of all sizes. Extension to general likelihoods, including forsurvival outcomes, greatly increases the applicability of the method. R andStan code is provided, and the methods are implemented in the multinma Rpackage.

Multilevel network meta-regression for general likelihoods: synthesis of individual and aggregate data with applications to survival analysis